Ontology-Based Structured Cosine Similarity in Speech Document Summarization

Development of algorithms for automated text categorization in massive text document sets is an important research area of data mining and knowledge discovery. Most of the text-clustering methods were grounded in the term-based measurement of distance or similarity, ignoring the structure of terms in documents. In this paper we present a novel method named Structured Cosine Similarity that furnishes document clustering with a new way of modeling on document summarization, considering the structure of terms in documents in order to improve the quality of speech document clustering.